abstract = "Image analysis is a key area in the computer vision
domain that has many applications. Genetic Programming
(GP) has been successfully applied to this area
extensively, with promising results. High-level
features extracted from methods such as Speeded Up
Robust Features (SURF) and Histogram of Oriented
Gradients (HoG) are commonly used for object detection
with machine learning techniques. However, GP
techniques are not often used with these methods,
despite being applied extensively to image analysis
problems. Combining the training process of GP with the
powerful features extracted by SURF or HoG has the
potential to improve the performance by generating
high-level, domain-tailored features. This paper
proposes a new GP method that automatically detects
different regions of an image, extracts HoG features
from those regions, and simultaneously evolves a
classifier for image classification. By extending an
existing GP region selection approach to incorporate
the HoG algorithm, we present a novel way of using
high-level features with GP for image classification.
The ability of GP to explore a large search space in an
efficient manner allows all stages of the new method to
be optimised simultaneously, unlike in existing
approaches. The new approach is applied across a range
of datasets, with promising results when compared to a
variety of well-known machine learning techniques. Some
high-performing GP individuals are analysed to give
insight into how GP can effectively be used with
high-level features for image classification.